In the affine projection adaptive filtering algorithm, convergence is sped up by increasing the projection order but with an unwelcome consequence of increased steady-state misalignment. To address this unfavorable compromise, we propose a new affine projection algorithm with selective projections. This algorithm adaptively changes the projection order according to the estimated variance of the filter output error. The error variance is estimated using exponential window averaging with a variable forgetting factor and a simple moving averaging technique. The input regressors are selected according to two different criteria to update the filter coefficients at each iteration. Simulations, carried out for different adaptive filtering applications, demonstrate that the new algorithm provides fast initial convergence and low steady-state misalignment without necessarily trading off one for the other in addition to a significant reduction in average computational complexity.